library(tidyverse)
library(plotly)
f1 <- read_csv("https://github.com/ruhani13/Dataset/raw/main/race_wins_1950-2020.csv",show_col_types = FALSE)
New names:
* `` -> ...1
constructors<-read_csv("https://github.com/ruhani13/Dataset/raw/main/constructors_championship_1958-2020.csv",show_col_types = FALSE) %>%
filter(Year %in% (2011:2020))
top_drivers<-f1 %>%
group_by(Name) %>%
summarise(n=n()) %>%
arrange(desc(n)) %>%
top_n(15,n)
best_drivers<-top_drivers %>%
ungroup() %>%
pull(Name)
top_drivers<-filter(f1,Name %in% best_drivers)
p<-ggplot(top_drivers,aes(x = forcats::fct_rev(fct_infreq(Name)), fill = Team,text=paste("Race Wins: ", ..count..)))+
geom_bar() +
coord_flip()+
xlab("Drivers")+
ylab("Race Wins")+
ggtitle("Best Driver since 1950 with most race wins")+
theme_classic()+
viridis::scale_fill_viridis(discrete=T,option = "D")
ggplotly(p,tooltip = c("Team","text")) %>%
hide_legend()
constructors_champs<-constructors %>%
group_by(Team) %>%
summarise(n=sum(Points) )%>%
arrange(desc(n)) %>%
top_n(10,n)
best_teams<-constructors_champs %>%
ungroup() %>%
pull(Team)
constructors_champs<-filter(constructors,Team %in% best_teams)
p<-ggplot(constructors_champs,aes(as.factor(Year),Points,col=Team,text=paste("Position: ",Position)))+
geom_point(aes(size=Points))+
xlab("Year")+
ggtitle("F1 constructor standings")+
scale_colour_viridis_d(option = "plasma")+
theme_classic()
ggplotly(p,tooltip = c("Team","y","text")) %>%
layout(legend = list(orientation = 'h',y=-0.2))
NA
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